Statistical Foundations of Machine Learning by Gianluca Bontempi
Statistical Foundations of Machine Learning - Table of Contents
- 1. Introduction
- 2. Foundations of Probability
- 3. Parametric Estimation: The Classical Approach
- 4. Nonparametric Estimation and Testing
- 5. Statistical Supervised Learning
- 6. The Machine Learning Procedure
- 7. Linear Approaches
- 8. Nonlinear Approaches
- 9. Model Averaging Approaches
- 10. Feature Selection
- 11. Conclusions
- A. Unsupervised Learning
- B. Linear Algebra Notions
- C. Optimisation Notions
- D. Probabilistic Notions
- E. Plug-in Estimators
- F. Kernel Functions
- G. Companion R Package
- H. Companion R Shiny Dashboards
What You Will Learn in Statistical Foundations of Machine Learning
Statistical Foundations of Machine Learning by Gianluca Bontempi is an acclaimed university-level textbook designed to provide students with a solid, mathematically sound bridge between classical statistics and automated pattern recognition. This extensive text breaks down core machine learning concepts like nonparametric estimation, structural risk minimization, cross-validation, feature selection, and ensemble learning into clear, step-by-step analytical lessons.
Perfect for undergraduate and graduate students in computer science, artificial intelligence, data engineering, and applied statistics, this textbook balances theoretical statistical learning principles with practical algorithmic code. Professor Bontempi systematically presents learning algorithms—including linear regression, support vector machines, neural networks, decision trees, and time series predictors—through the unifying lens of information theory and statistical decision theory. Whether you are analyzing generalization error bounds or tuning complex hyper-parameters, this book offers a structured roadmap through every topic.
Recognized globally for its clarity, pedagogical value, and computational focus, it remains one of the best statistical machine learning books pdf available for self-study. It systematically equips readers with the necessary analytical tools for mastering machine learning foundations with confidence.
Book Details & Specifications
Title:
Statistical Foundations of Machine Learning by Gianluca Bontempi
Publisher:
Université Libre de Bruxelles (ULB)
Year:
2022
Pages:
364
Type:
PDF
Language:
English
ISBN-10 #:
B0D5HDT6H6
ISBN-13 #:
License:
External Educational Resource
Amazon:
Amazon
About the Author: Gianluca Bontempi
The author Gianluca Bontempi
is a Full Professor in the Computer Science Department at Université Libre de Bruxelles (ULB) in Belgium and co-director of the Machine Learning Group (MLG). He earned his Ph.D. from the Politecnico di Milano, specializing in statistical learning, time series forecasting, and bioinformatics.
An active researcher and educator, Professor Bontempi has authored numerous scientific publications and open-source software tools. His work on statistical learning theory and predictive analytics equips students and researchers worldwide with a clear, mathematically rigorous foundation in modern machine learning.
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